Hybrid models with deep and invertible features
We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets|features), the predictive distr...
| Autores principales: | , , , , |
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| Formato: | Conference item |
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Proceedings of Machine Learning Research
2019
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